I am using Python 3.8, Prodigy 1.11.8, Spacy 3.4.0. I also downloaded the latest version of en_core_web_trf.
I created my model 'en_tagger_parser_trf' using prodigy the following code:
The model was trained with the following command:
prodigy train --ner gdpr_ner ./tmp_model_7 --eval-split 0.2 --config configAccuracy.cfg --label-stats --gpu-id 0 --base-model en_tagger_parser_trf
I attempt to load the model using:
hi @ruiyeNLP!
Can you explain what you're doing here?
I don't understand why you're loading en_core_web_lg but calling it a _trf since it would only have tok2vec layer, not a transformers. Maybe that's just a typo.
Are you trying to simply reuse a full pipeline but then create your own ner?
I found this post in spaCy GitHub Discussion forum with a similar error.
It recommends:
nlp = spacy.load("en_core_web_lg", exclude=["ner"]) # could be en_core_web_trf
nlp.add_pipe("ner", source=spacy.load("my_custom_pipeline"))
Could you try this?